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Finding REMO — Detecting Relative Motion Patterns in Geospatial Lifelines

  • Conference paper
Developments in Spatial Data Handling

Abstract

Technological advances in position aware devices increase the availability of tracking data of everyday objects such as animals, vehicles, people or football players. We propose a geographic data mining approach to detect generic aggregation patterns such as flocking behaviour and convergence in geospatial lifeline data. Our approach considers the object’s motion properties in an analytical space as well as spatial constraints of the object’s lifelines in geographic space. We discuss the geometric properties of the formalised patterns with respect to their efficient computation.

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References

  • Aurenhammer F (1991) Voronoi diagrams: A survey of a fundamental geometric data structure. ACM Comput. Surv. 23(3):345–405

    Article  Google Scholar 

  • Batty M, Desyllas J, Duxbury E (2003) The discrete dynamics of small-scale spatial events: agent-based models of mobility in carnivals and street parades. Int. J. Geographical Information Systems 17(7):673–697

    Google Scholar 

  • Bern M, Eppstein D (1997) Approximation algorithms for geometric problems. In Hochbaum DS (ed) Approximation Algorithms for NP-Hard Problems, PWS Publishing Company, Boston, MA, pp 296–345

    Google Scholar 

  • Casaer J, Hermy M, Coppin P, Verhagen R (1999) Analysing space use patterns by Thiessen polygon and triangulated irregular network interpolation: a nonparametric method for processing telemetric animal fixes. Int. J. Geographical Information Systems 13(5):499–511

    Google Scholar 

  • de Berg M, van Kreveld M, Overmars M, Schwarzkopf O (2000) Computational Geometry — Algorithms and Applications. Springer, Berlin, 2nd edition

    Google Scholar 

  • Estivill-Castro V, Lee I (2002) Multi-level clustering and its visualization for exploratory data analysis. GeoInformatica 6(2):123–152

    Article  Google Scholar 

  • Ganskopp, D. (2001) Manipulating cattle distribution with salt and water in large arid-land pastures: a GPS/GIS assessment. Applied Animal Behaviour Science 73(4):251–262

    Article  Google Scholar 

  • Hornsby K, Egenhofer M (2002) Modeling moving objects over multiple granularities. Annals of Mathematics and Artificial Intelligence 36(1–2):177–194

    Google Scholar 

  • Iwase S, Saito H (2002) Tracking soccer player using multiple views. In IAPR Workshop on Machine Vision Applications, MVA Proceedings, pp 102–105

    Google Scholar 

  • Jain A, Duin R, Mao J (2000) Statistical pattern recognition: A review. IEEE Transactions on Pattern Recognition and Machine Intelligence 22(1):4–37

    Google Scholar 

  • Laube P, Imfeld S (2002) Analyzing relative motion within groups of trackable moving point objects. In: Egenhofer M, Mark D (eds), Geographic Information Science, Second International Conference, GIScience 2002, Boulder, CO, USA, September 2002, LNCS 2478, Springer, Berlin, pp 132–144

    Google Scholar 

  • Mark, D (2003) Geographic information science: Defining the field. In: Duckham M, Goodchild M, Worboys M (eds), Foundations of Geographic Information Science, chap. 1, Taylor and Francis, London New York, pp 3–18

    Google Scholar 

  • Miller H (2003) What about people in geographic information science? Computers, Environment and Urban Systems 27(5):447–453

    Article  Google Scholar 

  • Miller H (2004) Tobler’s first law and spatial analysis. in preparation.

    Google Scholar 

  • Miller H, Han J (2001) Geographic data mining and knowledge discovery: An overview. In: Miller H, Han J (eds) Geographic data mining and knowledge discovery, Taylor and Francis, London New York, pp 3–32

    Google Scholar 

  • Miller H, Wu Y (2000) GIS software for measuring space-time accessibility in transportation planning and analysis. GeoInformatica 4(2):141–159

    Article  Google Scholar 

  • Mountain D, Raper J (2001) Modelling human spatio-temporal behaviour: A challenge for location-based services. Proceedings of GeoComputation, Brisbane, 6

    Google Scholar 

  • Openshaw S (1994) Two exploratory space-time-attribute pattern analysers relevant to GIS. In: Fotheringham S, Gogerson P (eds) GIS and Spatial Analysis, chap. 5, Taylor and Francis, London New York, pp 83–104

    Google Scholar 

  • Openshaw S, Turton I, MacGill J (1999) Using geographic analysis machine to analyze limiting long-term illness census data. Geographical and Environmental Modelling 3(1):83–99

    Google Scholar 

  • Pfoser D, Jensen C (1999) Capturing the uncertainty of moving-object representations. In: Gueting R, Papadias D, Lochowsky, F (eds) Advances in Spatial Databases, 6th International Symposium, SSD’99, Hong Kong, China, July 1999. LNCS 1651, Springer, Berlin Heidelberg, pp 111–131

    Google Scholar 

  • Ramos E (1999) On range reporting, ray shooting and k-level construction. In: Proc. 15th Annu. ACM Symp. on Computational Geometry, pp 390–399

    Google Scholar 

  • Roddick J, Hornsby K, Spiliopoulou M (2001) An updated bibliography of temporal, spatial, and spatio-temporal data mining research. In: Roddick J, Hornsby K (eds), Temporal, spatial and spatio-temporal data mining, TSDM 2000, LNAI 2007, Springer, Berlin Heidelberg, pp 147–163

    Google Scholar 

  • Sibbald AM, Hooper R, Gordon IJ, Cumming S (2001) Using GPS to study the effect of human disturbance on the behaviour of the red deer stags on a highland estate in Scotland. In: Sibbald A, and Gordon IJ (eds) Tracking Animals with GPS, Macaulay Institute, pp 39–43

    Google Scholar 

  • Smyth C (2001) Mining mobile trajectories. In: Miller H, Han J (eds) Geographic data mining and knowledge discovery, Taylor and Francis, London New York, pp 337–361

    Google Scholar 

  • Tobler W (1970) A computer movie simulating urban growth in the Detroit region. Economic Geography 46(2):234–240

    Google Scholar 

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© 2005 Springer-Verlag Berlin Heidelberg

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Laube, P., van Kreveld, M., Imfeld, S. (2005). Finding REMO — Detecting Relative Motion Patterns in Geospatial Lifelines. In: Developments in Spatial Data Handling. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-26772-7_16

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